基于Sentinel-2时序数据的新疆焉耆盆地农作物遥感识别与评估
收稿日期: 2023-06-06
修回日期: 2023-07-24
网络出版日期: 2024-05-17
基金资助
国家自然科学基金项目(U1703341);国家自然科学基金项目(41764003);自治区科技创新基地建设计划项目(2020D04039)
Remote sensing identification and assessment of crops in the Yanqi Basin, Xinjiang, China based on Sentinel-2 time series data
Received date: 2023-06-06
Revised date: 2023-07-24
Online published: 2024-05-17
为及时准确地获取干旱区农作物种植信息，研究借助PIE-Engine Studio平台，以新疆焉耆盆地为研究区，基于2022年Sentinel-2影像和1948个野外定位采样数据提取农作物生育期内14种植被指数，使用See5.0决策树、随机森林（Random forest，RF）和多元回归（Multiple regression，MR）模型优选特征参数，结合支持向量机（Support vector machine，SVM）算法构建5种分类模型和5种样方分割方案进行农作物种植信息提取，通过目视解译和混淆矩阵对比分析分类结果，确定最佳分类方案。结果表明：（1） 所有分类模型的总体精度（OA）和Kappa系数均在92.20%和0.9037以上，说明在PIE平台中使用SVM算法提取农作物信息是可行的。（2） SVM-有红边的OA和Kappa系数均值为93.77%和0.9236，比SVM-无红边方法提高了0.96%和0.0120。（3） 相比于SVM-有红边方法，植被指数的引入提高了SVM-RF、SVM-MR和SVM-See5.0的OA和Kappa系数。（4） 5种分类模型的OA和Kappa系数均值的大小关系为：SVM-RF>SVM-MR>SVM-See5.0>SVM-有红边>SVM-无红边，表明红边波段和植被指数的加入显著提高了农作物识别的精度，其中SVM-RF（8:2）为最佳分类模型，OA和Kappa系数分别为98.72%和0.9866。研究结果可为准确快速获取大尺度干旱区农作物信息提供新的思路和参考依据。
关键词： 农作物; Sentinel-2; 支持向量机; PIE-Engine Studio; 焉耆盆地
张旭辉 , 玉素甫江·如素力 , 仇忠丽 , 亚夏尔·艾斯克尔 , 阿卜杜热合曼·吾斯曼 . 基于Sentinel-2时序数据的新疆焉耆盆地农作物遥感识别与评估[J]. 干旱区地理, 2024 , 47(4) : 672 -683 . DOI: 10.12118/j.issn.1000－6060.2023.262
To obtain timely and accurate information about crop cultivation in arid zones, this study used the PIE-Engine Studio platform to extract 14 vegetation indices in the Yanqi Basin, Xinjiang, China based on 2022 Sentinel-2 images and 1948 field location sampling data during the crop reproduction period. Crop planting information was extracted using the See5.0 decision tree, random forest (RF), and multiple regression (MR) models to select feature parameters. Each model was combined with support vector machine (SVM) algorithms to construct five classification models and five sample segmentation schemes. The best classification scheme was determined by visual interpretation and confusion matrix comparison. The results are as follows: (1) The overall accuracy (OA) and Kappa coefficients of all classification models are above 92.20% and 0.9037, respectively, indicating that it is feasible to extract crop information using the SVM algorithm in the PIE platform. (2) The mean OA and Kappa coefficients of SVM-with-red-edge are 93.77% and 0.9236, which are 0.96% and 0.0120, respectively. (3) The introduction of vegetation index improved the OA and Kappa coefficients of SVM-RF, SVM-MR, and SVM-See5.0 compared with the SVM-with-red-edge method. (4) The mean OA and Kappa coefficient relationships for the five classification models were SVM-RF>SVM-MR>SVM-See5.0>SVM-with-red-edge>SVM-without-red-edge, showing that the inclusion of the red-edge band and vegetation index significantly improved the accuracy of crop identification, with SVM-RF (8:2) being the best classification model with OA and Kappa coefficients of 98.72% and 0.9866, respectively. These results provide new ideas and references for accurate and rapid access to large-scale arid zone crop information.
Key words： crop; Sentinel-2; support vector machines; PIE-Engine Studio; Yanqi Basin
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